Transforming clinical documentation with ambient artificial intelligence (AI) scribes: a narrative review of...

Electronic health records (EHRs) have modernized care but increased documentation burden and clinician burnout. Ambient artificial intelligence (AI) scribes, combining automated speech recognition (ASR), natural language processing (NLP), and generative AI, aim to address this by capturing encounters and generating documentation. Related technologies, including virtual assistants and autonomous patient-facing systems, extend these capabilities beyond the clinician's physical presence. This narrative review synthesizes current evidence on the real-world performance, implementation, and impact of these AI tools.

A narrative literature search was conducted using PubMed, supplemented by a manual review of reference lists from key articles. The search covered studies published between January 2019 and June 2025. After screening and full-text review, 18 studies met inclusion criteria and were incorporated into this review.

AI scribes consistently reduce documentation burden and cognitive load, improve workflow efficiency, save time, and enhance patient-clinician interaction by allowing greater clinician focus. However, studies also report frequent documentation omissions and occasional clinically significant hallucinations. Implementation remains a sociotechnical challenge involving workflow redesign, medico-legal considerations, and preservation of the patient-clinician relationship. In cardiology, where documentation requires precise, time-sensitive detail, AI-related errors may carry greater risk, underscoring the need for specialty-specific validation.

Ambient AI scribes show promise in reducing workload, improving efficiency, and decreasing burnout, but current systems still generate high omission rates and intermittent factual inaccuracies that may affect clinical decision-making. Evidence remains limited by small cohorts and methodological variability, warranting cautious interpretation. More rigorous, standardized evaluations are needed before routine clinical adoption.